Citations

Abstract Aims Sodium-glucose cotransporter 2 inhibitors (SGLT2-Is) improve heart failure (HF) outcomes but their effects on acute myocardial infarction (AMI) remain poorly characterized. This study aimed to evaluate the 1-year cardiovascular outcomes of SGLT2-Is among patients with AMI. Methods and results We conducted an observational, retrospective cohort study using TriNetX data, including patients aged ≥18 with AMI identified via ICD-10 codes regardless of left ventricular ejection fraction (LVEF), categorized by SGLT2-Is use. Propensity score matching (PSM) was performed to balance baseline demographics, comorbidities, and medication use. Adjusted odds ratios (aORs) were estimated for the primary outcome (recurrent AMI) and the secondary outcomes (acute HF hospitalizations, stroke, all-cause hospitalizations, all-cause mortality, new-onset atrial fibrillation, and cardiac arrest). After PSM, 89 554 patients were analysed (44 777 SGLT2-Is users; 44 777 non-users). The mean age was ∼68 years in both cohorts with a similarly high burden of cardiovascular comorbidities. Mean follow-up duration was 290.854 days for SGLT2-Is users and 284.465 days for non-users. SGLT2-Is use was linked to lower rates of recurrent AMI [aOR: 0.459; 95% confidence interval (CI): 0.367–0.551], all-cause hospitalizations (aOR: 0.782; 95% CI: 0.762–0.803), all-cause mortality (aOR: 0.640; 95% CI: 0.612–0.670), and cardiac arrest (aOR: 0.834; 95% CI: 0.773–0.900). No differences were observed in acute HF hospitalizations, new-onset atrial fibrillation, or stroke. Conclusion SGLT2-Is are associated with improved cardiovascular outcomes in patients with AMI, including reductions in recurrent AMI, all-cause hospitalizations and mortality, and cardiac arrest. These findings emphasize the need for prospective clinical trials involving patients with AMI and other cardiovascular comorbidities, regardless of LVEF, to confirm these results.

Cardiac computed tomography (CT) has rapidly advanced, becoming an invaluable tool for diagnosing and prognosticating various cardiovascular diseases. While echocardiography and cardiac magnetic resonance imaging (CMR) remain the gold standards for myocardial assessment, modern CT technologies offer enhanced spatial resolution, making it an essential tool in clinical practice. Cardiac CT has expanded beyond coronary artery disease evaluation, now playing a key role in assessing cardiomyopathies and structural heart diseases. Innovations like photon-counting CT enable precise estimation of myocardial extracellular volume, facilitating the detection of infiltrative disorders and myocardial fibrosis. Additionally, CT-based myocardial strain analysis allows for the classification of impaired myocardial contractility, while quantifying cardiac volumes and function remains crucial in cardiomyopathy evaluation. This review explores the emerging role of cardiac CT in cardiomyopathy phenotyping, emphasizing recent technological advancements.

Background: Aortic stenosis (AS) imposes a significant mortality burden. Understanding demographic influences on the location of AS-related death is crucial for advancing equitable end-of-life care. Therefore, we investigated how demographic factors influence the location of death among AS patients in the United States. Methods: We completed a cross-sectional study utilizing US mortality data from the CDC’s WONDER database for 2019. All files related to decedents with AS identified as the primary cause of death were obtained, including demographic information and death locations (i.e., inpatient facilities, outpatient/ER facilities, home, or hospice/nursing facilities). Associations between demographic factors (age, sex, race/ethnicity, marital status, and education) and place of death were assessed using multivariable logistic regression models, yielding odds ratios (ORs). Results: In 2019, most AS-related deaths occurred in inpatient facilities (38.3%, n = 5062), home (29.2%, n = 3859), or hospice/nursing facilities (28.6%, n = 3775). Higher odds of inpatient death were observed among Black (OR 1.67, p < 0.001) and Hispanic individuals (OR 1.91, p < 0.001) compared to White decedents. Those aged >85 years were more likely to die at home (OR 1.76, p < 0.001) or in hospice/nursing facilities (OR 7.80, p < 0.001). Males had increased odds of inpatient death (OR 1.09, p = 0.044) but decreased odds of hospice/nursing facility death (OR 0.87, p = 0.003). Higher education levels were associated with increased odds of home death (OR 1.33, p = 0.023) and decreased odds of hospice/nursing facility death (OR 0.71, p = 0.015). Conclusions: Demographic factors significantly influence the location of death among AS patients, emphasizing the need for culturally and socioeconomically tailored interventions to promote equitable end-of-life care.

ABSTRACTBackgroundMyeloproliferative neoplasms (MPN) are associated with an increased cardiovascular risk including acute coronary syndrome. However, there is a lack of comprehensive data regarding the rate of percutaneous coronary intervention (PCI), as well as the in‐hospital characteristics and outcomes for MPN patients.AimsWe aimed to evaluate the temporal trends and outcomes of PCI among patients with MPN.Methods and ResultsThe National Inpatient Sample database from 2016 to 2020 was queried to identify all PCI hospitalizations. Temporal trends and outcomes of patients with and without MPN following PCI were analyzed. Propensity score matching (PSM) was implemented to compare outcomes between MPN and non‐MPN groups. 2,237,210 PCI hospitalizations with 7560 (0.27%) patients with MPN were included in this study. Throughout the study period, the prevalence of MPN among PCI admissions remained stable (p‐value for trend = 0.12). Within the MPN subgroup, essential thrombocythemia (ET) was the predominant condition (53.2). Patients with MPN had higher prevalence of cardiovascular comorbidities than non‐MPN patients. Following PSM, MPNs were significantly associated with a higher risk of blood transfusions (OR: 1.66, 95% CI: 1.22−2.24, p = 0.001) and AKI (OR: 1.39, 95% CI: 1.17−1.65, p < 0.001). In contrast, the risk of in‐hospital mortality (OR: 1.18, 95% CI: 0.83−1.69, p = 0.354 and bleeding (OR: 1.43, 95% CI: 0.90−2.27, p = 0.127) did not significantly differ between the two groups.ConclusionsOur study demonstrated that while the prevalence of MPN among patients undergoing PCI remained stable, those with MPN faced higher risks of bleeding, blood transfusion and acute kidney injury. Further research is warranted to explore the underlying reasons for these increased risks and to improve outcomes in this high‐risk group.

Cardiovascular diseases and cancer are the leading causes of morbidity and mortality in modern society. Expanding cancer therapies that have improved prognosis may also be associated with cardiotoxicity, and extended life span after survivorship is associated with the increasing prevalence of cardiovascular disease. As such, the field of cardio-oncology has been rapidly expanding, with an aim to identify cardiotoxicity and cardiac disease early in a patient who is receiving treatment for cancer or is in survivorship. Artificial intelligence is revolutionizing modern medicine with its ability to identify cardiac disease early. This article comprehensively reviews applications of artificial intelligence specifically applied to electrocardiograms, echocardiography, cardiac magnetic resonance imaging, and nuclear imaging to predict cardiac toxicity in the setting of cancer therapies, with a view to reduce early complications and cardiac side effects from cancer therapies such as chemotherapy, radiation therapy, or immunotherapy.

Abstract Aims Atrial fibrillation (AF) is the most common cardiac arrhythmia. Although lipoprotein(a) [Lp(a)] is known to be a well-established risk factor for atherosclerotic cardiovascular disease, its role in the development of AF, independent of this association, remains unclear. Methods and results Adult patients from the three Mayo Clinic sites with a baseline Lp(a) and without AF history were included. Patients were categorized into two groups based on their Lp(a) levels: high Lp(a) (≥50 mg/dL) and low Lp(a) (&lt;50 mg/dL). Survival probabilities free from incident AF were compared between Lp(a) groups, during a follow-up period up to 15 years, using the Kaplan–Meier curve and the log-rank test. Multivariable Cox regression analysis was also conducted. A total of 75 376 patients were included (median age: 55 years, 59% males), with a median follow-up duration of 8.8 (inter-quartile range: 3.4, 14.8) years. Incident AF was detected in 5738 (7.6%) patients. Survival probability free from incident AF was significantly lower in patients with elevated Lp(a) (86%) compared with those with low Lp(a) (88%, log-rank P &lt; 0.001). Multivariable analysis adjusted for potential risk factors of AF showed a statistically significant association of elevated Lp(a) with an 11% increase in AF risk (adjusted hazard ratio: 1.11, 95% confidence interval: 1.05–1.18). Conclusion Our study suggests that elevated Lp(a) (≥50 mg/dL) is an independent risk factor for incident AF. Future prospective studies are warranted to validate our results and to test if reducing Lp(a) could mitigate the burden of AF.

Background: Pulmonary hypertension (PH) is a complex condition associated with significant morbidity and mortality. Traditional diagnostic and management approaches for PH often face limitations, leading to delays in diagnosis and potentially suboptimal treatment outcomes. Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) offers a transformative approach to PH care. Materials and Methods: We systematically searched PubMed, Scopus, and Web of Science for original studies on AI applications in PH, using predefined keywords. Out of more than 500 initial articles, 45 relevant studies were selected. Risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool). Results: This review examines the potential applications of AI in PH, focusing on its role in enhancing diagnosis, disease classification, and prognostication. We discuss how AI-powered analysis of medical data can improve the accuracy and efficiency of detecting PH. Furthermore, we explore the potential of AI in risk stratification, leading to treatment optimization for PH. Conclusions: While acknowledging the existing challenges and limitations and the need for continued exploration and refinement of AI-driven tools, this review highlights the significant promise of AI in revolutionizing PH management to improve patient outcomes.

Abstract Aims The increasing need for diagnostic echocardiography tests presents challenges in preserving the quality and promptness of reports. While Large Language Models (LLMs) have proven effective in summarizing clinical texts, their application in echo remains underexplored. Methods and results Adult echocardiography studies, conducted at the Mayo Clinic from 1 January 2017 to 31 December 2017, were categorized into two groups: development (all Mayo locations except Arizona) and Arizona validation sets. We adapted open-source LLMs (Llama-2, MedAlpaca, Zephyr, and Flan-T5) using In-Context Learning and Quantized Low-Rank Adaptation fine-tuning (FT) for echo report summarization from ‘Findings’ to ‘Impressions.’ Against cardiologist-generated Impressions, the models’ performance was assessed both quantitatively with automatic metrics and qualitatively by cardiologists. The development dataset included 97 506 reports from 71 717 unique patients, predominantly male (55.4%), with an average age of 64.3 ± 15.8 years. EchoGPT, a fine-tuned Llama-2 model, outperformed other models with win rates ranging from 87% to 99% in various automatic metrics, and produced reports comparable to cardiologists in qualitative review (significantly preferred in conciseness (P &lt; 0.001), with no significant preference in completeness, correctness, and clinical utility). Correlations between automatic and human metrics were fair to modest, with the best being RadGraph F1 scores vs. clinical utility (r = 0.42) and automatic metrics showed insensitivity (0–5% drop) to changes in measurement numbers. Conclusion EchoGPT can generate draft reports for human review and approval, helping to streamline the workflow. However, scalable evaluation approaches dedicated to echo reports remains necessary.

Introduction/Background: Loeys-Dietz syndrome (LDS) is a rare autosomal dominant aortic aneurysm syndrome characterized by arterial tortuosity resulting in arterial aneurysms and dissections, with unique craniofacial and skeletal features including bifid uvula, cleft palate and hypertelorism. While the presence of atrial fibrillation (Afib) and arrhythmias have been reported in LDS patients, limited investigation has been completed regarding the associated risk and severity of vascular complications in relationship to these arrhythmias1,2. Research Questions/Hypothesis: Are there any differences in vascular outcomes in patients with LDS when experiencing various forms of tachyarrhythmia? Goals/Aims: To evaluate the relationship between various types of arrhythmias and vascular outcomes in patients with LDS, including arterial dissection, stroke, revascularization, and death. Methods/Approach: A large sample of LDS patients (n=436) from a single tertiary referral center was assessed for ICD-10 diagnoses of arrhythmia and outcomes of interest. Arrhythmias assessed included Afib, atrial flutter (AFL), premature ventricular contractions (PVCs), and supraventricular tachycardia (SVT). Outcomes were arterial dissection, hemorrhagic and ischemic stroke, revascularization, and all-cause mortality. Results/Data: Our cohort (n=436) had an average age of 47 and was 57.8% female. A total of 26 patients had a documented history of tachyarrhythmia: 16 (3.4%) Afib, 2 (0.5%) AFL, 6 (1.4%) SVT, and 2 (0.5%) PVC. LDS patients with Afib were at significantly higher risk of arterial dissection (25% vs 6.0%, OR: 5.3, p<0.01), revascularization (18.8% vs 3.3%, OR: 8.1, p<0.01), stroke (25% vs 3.6%, OR: 9.0, p<0.001), and death (25% vs 4.3%, OR: 7.4, p<0.01) compared to those without a documented history of arrhythmia. Other arrhythmia types evaluated were not significant. Predictors of any arrhythmia type included hypertension (78% vs 31%, OR: 1.5, p<0.001) and heart failure (39% vs 5.0%, OR: 2.3, p<0.001). Conclusion: Although rare, Afib in LDS patients is associated with adverse vascular outcomes and mortality compared to those without a history of arrhythmia. Furthermore, the magnitude of increased risk is striking. Early identification and management of Afib in LDS patients may be critical in mitigating the risk of potential vascular complications.

Background: Atrial fibrillation (AF), the most common clinically managed cardiac arrhythmia, is known to increase the risk of heart failure, stroke, and mortality. Lipoprotein(a) [Lp(a)], a well-established risk factor for atherosclerotic cardiovascular disease (ASCVD), also plays a role in cardiovascular health. However, the influence of Lp(a) on the development of AF, independent of its association with ASCVD, remains unclear. Hypothesis: Elevated Lp(a) may be associated with increased risk of new-onset AF. Methods: A retrospective study was conducted on patients from the three Mayo Clinic sites who had their Lp(a) levels measured and had no prior history of AF. Patients were categorized into two groups based on their serum Lp(a) concentrations: high Lp(a) (≥50 mg/dL) and low Lp(a) (<50 mg/dL). New AF diagnoses were identified during the follow-up period. Survival probabilities between the two Lp(a) groups were compared using the Kaplan–Meier curve and the Log-Rank test. Cox regression analyses, both univariable and multivariable, were performed to evaluate established risk factors and compare the risk of new-onset AF between the high and low Lp(a) groups. Results: A total of 108,930 patients were included (median age: 55 years, 58% males), with a follow-up duration for up to 10 years. Incident AF was detected in 8432 (7.7%) patients. Median serum levels of Lp(a) were significantly higher in patients with AF versus non-AF patients: 15 (IQR 43) vs. 12 (IQR 34) mg/dL, p<0.001. The percentage of patients having elevated Lp(a) was significantly higher in the AF group versus non-AF group: 2119 (25%) vs 21151 (21%), p<0.001. Survival probability for new-onset AF was significantly lower in patients with elevated Lp(a) compared with those with low Lp(a) (log rank p<0.001, Figure 1). In the univariate analysis, high Lp(a) levels were associated with a 17% increase in the risk of AF (HR = 1.17, 95% CIs: 1.12 to 1.23). The multivariate analysis adjusted for well-established risk factors for AF showed a less strong but still significant association, with a 10% increase in AF risk (HR = 1.10, 95% CI: 1.05 to 1.15). Conclusion: Our study suggests that elevated Lp(a) (≥50 mg/dL) seems to be an independent risk factor for new-onset AF. Future prospective studies are still warranted to validate our results.